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Comparative Study of Machine Learning Algorithms for Feature Extraction and Prediction of Bacterial Leaf Blight Disease in Paddy Fields 8

Comparative Study of Machine Learning Algorithms for Feature...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Krishna, Penugonda Seetha Rama Nagarajan, S. Annamalai University Dept. of Computer Science Engineering Tamilnadu Chidambaram India Government College of Engineering Srirangam Department of Computer Science and Engineering Trichy620012 India

ISBN: (纸本)9798350386578

Paddy farming plays a critical role in global food security, yet diseases like Bacterial Leaf Blight (BLB) pose significant threats to crop yield and quality. Early detection and accurate diagnosis are essential for effective disease management. This research study investigates machine learning methods for feature extraction and prediction of BLB in paddy fields. Utilizing a comprehensive dataset of paddy leaf images, several machine learning models including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), and Gradient Boosting Machine (GBM) were trained and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were employed to assess the effectiveness of each model. The comparative analysis discusses the strengths and limitations of each method, focusing on those that demonstrate superior capability in early and accurate detection of BLB. These findings provide valuable insights for agricultural scientists and practitioners to enhance disease management strategies using modern machine learning techniques. By leveraging this knowledge, specialists can optimize interventions and improve agricultural practices to mitigate the impact of diseases like BLB on paddy farming and ensure global food security. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor 2

IoT-Enabled Covert Surveillance Camera Identification throug...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ruksana, Shaik Imran, Shaik Farheen, Shaik Fatima Kireeti, Krovi Mohan Sailaja, K.L. VR Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India VR Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350371314

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adept.y spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all. © 2024 IEEE.

关键词： Cost effectiveness

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain 2

Decentralized Finance and Cross-Chain Interoperable Automate...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Sarumathi, S. Saxena, Apoorv Raj Saxena, Yash Raj Sana, Mehreen S. Md Verma, Saritha Roy, Sneha Hkbk College of Engineering Dept. of Computer Science Engineering Bangalore India ClayStack Bangalore India

ISBN: (纸本)9798350328202

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times. © 2024 IEEE.

关键词： Finance

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Ensemble Approaches for Offensive Language Detection in Kannada 3

Ensemble Approaches for Offensive Language Detection in Kann...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Monish, M.N. Sooda, Kavitha B.M.S. College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble's performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs. © 2024 IEEE.

关键词： Genetic algorithms

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Optimization of Cost-based Dynamic Economic Dispatch Problem using Mathematical Programming

Optimization of Cost-based Dynamic Economic Dispatch Problem...

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2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Mahmood, Mou Chowdhury, Nahid-Ur-Rahman Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This paper introduces a robust and effective methodology for addressing the dynamic economic dispatch (DED) optimization problem, which is pivotal in power system operations and planning. DED is basically an enhanced version of static economic dispatch (SED) problem that involves scheduling a set of generation units over a typical operational period, often spanning 24 hours, to cover the predicted load demand with the on/off status of each generation unit predetermined. The literature distinguishes between two main types of DED: cost-based and price-based. This paper focuses on the cost-based approach, utilizing quadratic constrained programming (QCP) to model the problem effectively. QCP is particularly suited for this task as it optimally manages the ramp rate constraints and capacities of the available generation units maintaining the minimum operating cost. To demonstrate the effectiveness of the proposed methodology, simulations were conducted using two distinct scenarios involving 5-unit and 10-unit power dispatch strategies. The results, obtained through the general algebraic modeling system (GAMS), clearly presents the superiority of GAMS in achieving optimal solutions when compared to other existing metaheuristic techniques. © 2024 IEEE.

关键词： Quadratic programming

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Advancements in Distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 IEEE.

关键词： Federated learning

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin Orchi, Nabiha Tasnim Alam, Md Ashraful Dipto, Shakib Mahmud Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dept. of Computer Science and Engineering Dhaka Bangladesh University of Liberal Arts Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Cvis Research Lab Dhaka Bangladesh

ISBN: (纸本)9798350350289

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses. © 2024 IEEE.

关键词： Citrus fruits

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